GenomeRNAi: a database for cell-based and in vivo RNAi phenotypes, 2013 update

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.

Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

Recent Progress on Nanocarriers for Topical-Mediated RNAi Strategies for Crop Protection—A Review

To fulfil the growing needs of the global population, sustainability in food production must be ensured. Insect pests and pathogens are primarily responsible for one-third of food losses and harmful synthetic pesticides have been applied to protect crops from these pests and other pathogens such as viruses and fungi. An alternative pathogen control mechanism that is more “friendly” to the environment can be developed by externally applying double-stranded RNAs (dsRNAs) to suppress gene expression. However, the use of dsRNA sprays in open fields is complicated with respect to variable efficiencies in the dsRNA delivery, and the stability of the dsRNA on and in the plants, and because the mechanisms of gene silencing may differ between plants and between different pathogen targets. Thus, nanocarrier delivery systems have been especially used with the goal of improving the efficacy of dsRNAs. Here, we highlight recent developments in nanoparticle-mediated nanocarriers to deliver dsRNA, including layered double hydroxide, carbon dots, carbon nanotubes, gold nanoparticles, chitosan nanoparticles, silica nanoparticles, liposomes, and cell-penetrating peptides, by review of the literature and patent landscape. The effects of nanoparticle size and surface modification on the dsRNA uptake efficiency in plants are also discussed. Finally, we emphasize the overall limitation of dsRNA sprays, the risks associated, and the potential safety concerns for spraying dsRNAs on crops.

Purriato is a conserved small open reading frame gene that interacts with the CASA pathway to regulate muscle homeostasis and epithelial tissue growth in Drosophila

Recent advances in proteogenomic techniques and bioinformatic pipelines have permitted the detection of thousands of translated small Open Reading Frames (smORFs), which contain less than 100 codons, in eukaryotic genomes. Hundreds of these actively translated smORFs display conserved sequence, structure and evolutionary signatures indicating that the translated peptides could fulfil important biological roles. Despite their abundance, only tens of smORF genes have been fully characterised; these act mainly as regulators of canonical proteins involved in essential cellular processes. Importantly, some of these smORFs display conserved functions with their mutations being associated with pathogenesis. Thus, investigating smORF roles in Drosophila will not only expand our understanding of their functions but it may have an impact in human health. Here we describe the function of a novel and essential Drosophila smORF gene named purriato (prto). prto belongs to an ancient gene family whose members have expanded throughout the Protostomia clade. prto encodes a transmembrane peptide which is localized in endo-lysosomes and perinuclear and plasma membranes. prto is dynamically expressed in mesodermal tissues and imaginal discs. Targeted prto knockdown (KD) in these organs results in changes in nuclear morphology and endo-lysosomal distributions correlating with the loss of sarcomeric homeostasis in muscles and reduction of mitosis in wing discs. Consequently, prto KD mutants display severe reduction of motility, and shorter wings. Finally, our genetic interaction experiments show that prto function is closely associated to the CASA pathway, a conserved mechanism involved in turnover of mis-folded proteins and linked to muscle dystrophies and neurodegenerative diseases. Thus, this study shows the relevance of smORFs in regulating important cellular functions and supports the systematic characterisation of this class of genes to understand their functions and evolution.

A genome-wide RNAi screen for genes important for proliferation of cultured Drosophila cells at low temperature identifies the Ball/VRK protein kinase

A change in ambient temperature is predicted to disrupt cellular homeostasis by affecting all cellular processes in an albeit non-uniform manner. Diffusion is generally less temperature-sensitive than enzymes, for example, and each enzyme has a characteristic individual temperature profile. The actual effects of temperature variation on cells are still poorly understood at the molecular level. Towards an improved understanding, we have performed a genome-wide RNA interference screen with S2R + cells. This Drosophila cell line proliferates over a temperature range comparable to that tolerated by the parental ectothermic organism. Based on effects on cell counts and cell cycle profile after knockdown at 27 and 17 °C, respectively, genes were identified with an apparent greater physiological significance at one or the other temperature. While 27 °C is close to the temperature optimum, the substantially lower 17 °C was chosen to identify genes important at low temperatures, which have received less attention compared to the heat shock response. Among a substantial number of screen hits, we validated a set successfully in cell culture and selected ballchen for further evaluation in the organism. This gene encodes the conserved metazoan VRK protein kinase that is crucial for the release of chromosomes from the nuclear envelope during mitosis. Our analyses in early embryos and larval wing imaginal discs confirmed a higher requirement for ballchen function at temperatures below the optimum. Overall, our experiments validate the genome-wide screen as a basis for future characterizations of genes with increased physiological significance at the lower end of the readily tolerated temperature range.

Wnt pathway inhibitors are upregulated in XLH dental pulp cells in response to odontogenic differentiation

X-linked hypophosphatemia (XLH) represents the most common form of familial hypophosphatemia. Although significant advances have been made in the treatment of bone pathology, patients undergoing therapy continue to experience significantly decreased oral health-related quality of life. The following study addresses this persistent oral disease by further investigating the effect of DMP1 expression on the differentiation of XLH dental pulp cells. Dental pulp cells were isolated from the third molars of XLH and healthy controls and stable transduction of full-length human DMP1 were achieved. RNA sequencing was performed to evaluate the genetic changes following the induction of odontogenic differentiation. RNAseq data shows the upregulation of inhibitors of the canonical Wnt pathway in XLH cells, while constitutive expression of full-length DMP1 in XLH cells reversed this effect during odontogenic differentiation. These results imply that inhibition of the canonical Wnt pathway may contribute to the pathophysiology of XLH and suggest a new therapeutic strategy for the management of oral disease.

The Revelation of Continuously Organized, Co-Overexpressed Protein-Coding Genes with Roles in Cellular Communications in Breast Cancer

Many human cancers, including breast cancer, are polygenic and involve the co-dysregulation of multiple regulatory molecules and pathways. Though the overexpression of genes and amplified chromosomal regions have been closely linked in breast cancer, the notion of the co-upregulation of genes at a single locus remains poorly described. Here, we describe the co-overexpression of 34 continuously organized protein-coding genes with diverse functions at 8q.24.3(143437655-144326919) in breast and other cancer types, the CanCord34 genes. In total, 10 out of 34 genes have not been reported to be overexpressed in breast cancer. Interestingly, the overexpression of CanCord34 genes is not necessarily associated with genomic amplification and is independent of hormonal or HER2 status in breast cancer. CanCord34 genes exhibit diverse known and predicted functions, including enzymatic activities, cell viability, multipotency, cancer stem cells, and secretory activities, including extracellular vesicles. The co-overexpression of 33 of the CanCord34 genes in a multivariant analysis was correlated with poor survival among patients with breast cancer. The analysis of the genome-wide RNAi functional screening, cell dependency fitness, and breast cancer stem cell databases indicated that three diverse overexpressed CanCord34 genes, including a component of spliceosome PUF60, a component of exosome complex EXOSC4, and a ribosomal biogenesis factor BOP1, shared roles in cell viability, cell fitness, and stem cell phenotypes. In addition, 17 of the CanCord34 genes were found in the microvesicles (MVs) secreted from the mesenchymal stem cells that were primed with MDA-MB-231 breast cancer cells. Since these MVs were important in the chemoresistance and dedifferentiation of breast cancer cells into cancer stem cells, these findings highlight the significance of the CanCord34 genes in cellular communications. In brief, the persistent co-overexpression of CanCord34 genes with diverse functions can lead to the dysregulation of complementary functions in breast cancer. In brief, the present study provides new insights into the polygenic nature of breast cancer and opens new research avenues for basic, preclinical, and therapeutic studies in human cancer.

miRNA expression patterns in blood leukocytes and milk somatic cells of goats infected with small ruminant lentivirus (SRLV)

The study aims to determine the selected miRNAs expression in milk somatic cells (MSC) and blood leukocytes (BL) of SRLV-seronegative (SRLV-SN) and SRLV-seropositive (SRLV-SP) goats. A functional in silico analysis of their target genes was also conducted. MiR-93-5p and miR-30e-5p were expressed only in BL, while miR-144 was expressed only in MSC, regardless of SRLV infection. In the SRLV-SP goats, higher miR-214-3p and miR-221-5p levels were found in the MSC than in the BL. Only miR-30e-5p was influenced by the lactation stage in BL in both groups, while only miR-93-5p was altered in BL of SRLV-SN goats. The target gene protein products exhibited contradictory functions, protecting the host from virus on the one hand and assisting viruses in their life cycle on the other. The differential expression of the miRNAs observed between the MSC and BL of SRLV-SP goats may suggest that the local immune response to the infection in the udder differs from the systemic response, and acts independently. Some miRNAs demonstrated different expression between lactation stages. It may be influenced by the metabolic burden occurring in early lactation and its peak. Some of the studied miRNAs may influence viral infection by regulating the expression of their target genes.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:  https://synlethdb.sist.shanghaitech.edu.cn/v2.

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Nucleolar and Coiled-Body Phosphoprotein 1 Is Associated With Stemness and Represents a Potential Therapeutic Target in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and lacks approved specific targeted therapies. One of the major reasons why TNBC is difficult to treat is the high proportion of cancer stem cells within the tumor tissue. Nucleolus is the location of ribosome biogenesis which is frequently overactivated in cancer cells and overactivation of ribosome biogenesis frequently drives the malignant transformation of cancer. Nucleolar and coiled-body phosphoprotein 1 (NOLC1) is a nucleolar protein responsible for nucleolus organization and rRNA synthesis and plays an important role in ribosome biogenesis. However, the correlation of NOLC1 expression with patient prognosis and its value as a therapeutic target have not been evaluated in TNBC. In the current study, based on bioinformatics analysis of the online databases, we found that the expression of NOLC1 was higher in breast cancer tissues than normal tissues, and NOLC1 was expressed at a higher level in TNBC than other subtypes of breast cancer. GSEA analysis revealed that stemness-related pathways were significantly enriched in breast cancer with high NOLC1 gene expression. Further analyses using gene expression profiling interactive analysis 2 (GEPIA2), tumor immune estimation resource (TIMER) and search tool for retrieval of interacting genes/proteins (STRING) demonstrated that NOLC1 was significantly associated with stemness in both all breast cancer and basal-like breast cancer/TNBC patients at both gene and protein levels. Knockdown of NOLC1 by siRNA decreased the protein level of the key stemness regulators MYC and ALDH and inhibited the sphere-forming capacity in TNBC cell line MDA-MB-231. Univariate and multivariate Cox regression analyses demonstrated that NOLC1 was an independent risk factor for overall survival in breast cancer. PrognoScan and Kaplan-Meier plotter analyses revealed that high expression of NOLC1 was associated with poor prognosis in both all breast cancer and TNBC patients. Further immunohistochemical analysis of breast cancer patient samples revealed that TNBC cells had a lower level of NOLC1 in the nucleus compared with non-TNBC cells. These findings suggest that NOLC1 is closely associated with the stemness properties of TNBC and represents a potential therapeutic target for TNBC.

Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Supervised Methods for Biomarker Detection from Microarray Experiments

Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.

A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis)

BACKGROUND

The salmon louse (Lepeophtheirus salmonis) is an obligate ectoparasitic copepod living on Atlantic salmon and other salmonids in the marine environment. Salmon lice cause a number of environmental problems and lead to large economical losses in aquaculture every year. In order to develop novel parasite control strategies, a better understanding of the mechanisms of moulting and development of the salmon louse at the transcriptional level is required.

METHODS

Three weighted gene co-expression networks were constructed based on the pairwise correlations of salmon louse gene expression profiles at different life stages. Network-based approaches and gene annotation information were applied to identify genes that might be important for the moulting and development of the salmon louse. RNA interference was performed for validation. Regulatory impact factors were calculated for all the transcription factor genes by examining the changes in co-expression patterns between transcription factor genes and deferentially expressed genes in middle stages and moulting stages.

RESULTS

Eight gene modules were predicted as important, and 10 genes from six of the eight modules have been found to show observable phenotypes in RNA interference experiments. We knocked down five hub genes from three modules and observed phenotypic consequences in all experiments. In the infection trial, no copepodids with a RAB1A-like gene knocked down were found on fish, while control samples developed to chalimus-1 larvae. Also, a FOXO-like transcription factor obtained highest scores in the regulatory impact factor calculation.

CONCLUSIONS

We propose a gene co-expression network-based approach to identify genes playing an important role in the moulting and development of salmon louse. The RNA interference experiments confirm the effectiveness of our approach and demonstrated the indispensable role of a RAB1A-like gene in the development of the salmon louse. We propose that our approach could be generalized to identify important genes associated with a phenotype of interest in other organisms.

Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning

Pathogens causing infections, and particularly when invading the host cells, require the host cell machinery for efficient regeneration and proliferation during infection. For their life cycle, host proteins are needed and these Host Dependency Factors (HDF) may serve as therapeutic targets. Several attempts have approached screening for HDF producing large lists of potential HDF with, however, only marginal overlap. To get consistency into the data of these experimental studies, we developed a machine learning pipeline. As a case study, we used publicly available lists of experimentally derived HDF from twelve different screening studies based on gene perturbation in Drosophila melanogaster cells or in vivo upon bacterial or protozoan infection. A total of 50,334 gene features were generated from diverse categories including their functional annotations, topology attributes in protein interaction networks, nucleotide and protein sequence features, homology properties and subcellular localization. Cross-validation revealed an excellent prediction performance. All feature categories contributed to the model. Predicted and experimentally derived HDF showed a good consistency when investigating their common cellular processes and function. Cellular processes and molecular function of these genes were highly enriched in membrane trafficking, particularly in the trans-Golgi network, cell cycle and the Rab GTPase binding family. Using our machine learning approach, we show that HDF in organisms can be predicted with high accuracy evidencing their common investigated characteristics. We elucidated cellular processes which are utilized by invading pathogens during infection. Finally, we provide a list of 208 novel HDF proposed for future experimental studies.

Role of Extracellular Vesicles in Compromising Cellular Resilience to Environmental Stressors

Extracellular vesicles (EVs), like exosomes, are nanosized membrane-enveloped vesicles containing different bioactive cargo, such as proteins, lipids, mRNA, miRNA, and other small regulatory RNAs. Cell-derived EVs, including EVs originating from stem cells, may capture components from damaged cells or cells impacted by therapeutic treatments. Interestingly, EVs derived from stem cells can be preconditioned to produce and secrete EVs with different therapeutic properties, particularly with respect to heat-shock proteins and other molecular cargo contents. This behavior is consistent with stem cells that also respond differently to various microenvironments. Heat-shock proteins play roles in cellular protection and mediate cellular resistance to radiotherapy, chemotherapy, and heat shock. This review highlights the possible roles EVs play in mediating cellular plasticity and survival when exposed to different physical and chemical stressors, with a special focus on the respiratory distress due to the air pollution.

Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning

Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential or non-essential for survival in each species. Recently, a range of features linked to gene essentiality have been inferred using a machine learning (ML)-based approach, allowing essentiality predictions within a species. Nevertheless, predictions between species are still elusive. Here, we undertake a comprehensive study using ML to discover and validate features of essential genes common to both C. elegans and D. melanogaster. We demonstrate that the cross-species prediction of gene essentiality is possible using a subset of features linked to nucleotide/protein sequences, protein orthology and subcellular localisation, single-cell RNA-seq, and histone methylation markers. Complementary analyses showed that essential genes are enriched for transcription and translation functions and are preferentially located away from heterochromatin regions of C. elegans and D. melanogaster chromosomes. The present work should enable the cross-prediction of essential genes between model and non-model metazoans.

Exploration of databases and methods supporting drug repurposing: a comprehensive survey

Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.

Aberrant KIF23 expression is associated with adverse clinical outcome and promotes cellular malignant behavior through the Wnt/β-catenin signaling pathway in Colorectal Cancer

Purpose: The aim of the present study was to reveal the clinicopathological significance and prognostic role of kinesin family member 23 (KIF23) in colorectal cancer (CRC) and characterize its biological function and the underlying mechanisms.

Methods: Bioinformatics analysis, immunohistochemistry, Western blot and qRT-PCR were utilized to investigate the expression of KIF23 in CRC tissues. The CCK-8 assay, wound healing assay and Matrigel assay were used to detect cell proliferation, migration and invasion in vitro. Western blot, immunofluorescence staining and cell function experiment were performed to explore the underlying mechanism.

Results: The overexpression of KIF23 was associated with T stage, N stage, M stage and TNM stage, and CRC patients with high KIF23 expression had a worse prognosis. KIF23 knockdown inhibits CRC cells proliferation, migration and invasion in vitro. The mechanism study determined that KIF23 activates the Wnt/β-catenin signaling pathway by promoting the nuclear translocation of β-catenin to regulate the malignant behavior of CRC cells.

Conclusion: These results suggest that KIF23 may act as a putative oncogene and a potential therapeutic target in CRC.

Combined RNA/tissue profiling identifies novel Cancer/testis genes

Cancer/Testis (CT) genes are induced in germ cells, repressed in somatic cells, and derepressed in somatic tumors, where these genes can contribute to cancer progression. CT gene identification requires data obtained using standardized protocols and technologies. This is a challenge because data for germ cells, gonads, normal somatic tissues, and a wide range of cancer samples stem from multiple sources and were generated over substantial periods of time. We carried out a GeneChip‐based RNA profiling analysis using our own data for testis and enriched germ cells, data for somatic cancers from the Expression Project for Oncology, and data for normal somatic tissues from the Gene Omnibus Repository. We identified 478 candidate loci that include known CT genes, numerous genes associated with oncogenic processes, and novel candidates that are not referenced in the Cancer/Testis Database (www.cta.lncc.br). We complemented RNA expression data at the protein level for SPESP1, GALNTL5, PDCL2, and C11orf42 using cancer tissue microarrays covering malignant tumors of breast, uterus, thyroid, and kidney, as well as published RNA profiling and immunohistochemical data provided by the Human Protein Atlas (www.proteinatlas.org). We report that combined RNA/tissue profiling identifies novel CT genes that may be of clinical interest as therapeutical targets or biomarkers. Our findings also highlight the challenges of detecting truly germ cell‐specific mRNAs and the proteins they encode in highly heterogenous testicular, somatic, and tumor tissues.

MaveQuest: a web resource for planning experimental tests of human variant effects

Summary

Fully realizing the promise of personalized medicine will require rapid and accurate classification of pathogenic human variation. Multiplexed assays of variant effect (MAVEs) can experimentally test nearly all possible variants in selected gene targets. Planning a MAVE study involves identifying target genes with clinical impact, and identifying scalable functional assays for that target. Here, we describe MaveQuest, a web-based resource enabling systematic variant effect mapping studies by identifying potential functional assays, disease phenotypes and clinical relevance for nearly all human protein-coding genes.

Availability and implementation

MaveQuest service: https://mavequest.varianteffect.org/. MaveQuest source code: https://github.com/kvnkuang/mavequest-front-end/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cancer biology functional genomics: From small RNAs to big dreams

The year 2021 marks the 20th anniversary of the first publications reporting the discovery of the gene silencing mechanism, RNA interference (RNAi) in mammalian cells. Along with the many studies that delineated the proteins and substrates that form the RNAi pathway, this finding changed our understanding of the posttranscriptional regulation of mammalian gene expression. Furthermore, the development of methods that exploited the RNAi pathway began the technological revolution that eventually enabled the interrogation of mammalian gene function-from a single gene to the whole genome-in only a few days. The needs of the cancer research community have driven much of this progress. In this perspective, we highlight milestones in the development and application of RNAi-based methods to study carcinogenesis. We discuss how RNAi-based functional genetic analysis of exemplar tumor suppressors and oncogenes furthered our understanding of cancer initiation and progression and explore how such studies formed the basis of genome-wide scale efforts to identify cancer or cancer-type specific vulnerabilities, including studies conducted in vivo. Furthermore, we examine how RNAi technologies have revealed new cancer-relevant molecular targets and the implications for cancer of the first RNAi-based drugs. Finally, we discuss the future of functional genetic analysis, highlighting the increasing availability of complementary approaches to analyze cancer gene function.

KIF23 enhances cell proliferation in pancreatic ductal adenocarcinoma and is a potent therapeutic target

Background

In recent research, high expression of kinesin family member 23 (KIF23), one of the kinesin motor proteins involved in the regulation of cytokinesis, has been shown to be related to poor prognosis in glioma and paclitaxel-resistant gastric cancer, as a results of the enhancement of proliferation, migration, and invasion. In this study, we analyzed the role of KIF23 in the progression of pancreatic ductal adenocarcinoma.

Methods

A bioinformatic method was used to analyze the KIF23 mRNA level in pancreatic tumor tissues compared with normal pancreatic tissues and to analyze the connection between high KIF23 expression and prognosis. We examined the expression of KIF23 using immunohistochemistry and analyzed the connection between the expression of KIF23 and clinicopathological features in pancreatic ductal adenocarcinoma patients. In addition, a colony formation assay, MTT assay, and western blot assay were performed in vitro, along with a mouse xenograft model in vivo, to analyze the effect of KIF23 on proliferation. Further, the correlation between KIF23 and CDCA8 was analyzed by TCGA and immunohistochemical data.

Results

Bioinformatic results showed that KIF23 mRNA expression was higher in pancreatic tumor tissues than in normal pancreatic tissues and a poor prognosis has been linked to the high expression of KIF23. Immunohistochemistry revealed that KIF23 was highly expressed at the protein level and high expression of KIF23 correlated with adverse clinicopathological features. Our experimental results demonstrated that knockdown of KIF23 could inhibit the proliferation of pancreatic cells. Further, a positive correlation between KIF23 and CDCA8 expression existed, and KIF23 might promote pancreatic cancer proliferation by affecting CDCA8 expression.

Conclusions

Our data showed that high expression of KIF23 is associated with a poor prognosis, and KIF23 might be a potential therapeutic target for pancreatic ductal adenocarcinoma.

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning

Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster

Characterizing genes that are critical for the survival of an organism (i.e. essential) is important to gain a deep understanding of the fundamental cellular and molecular mechanisms that sustain life. Functional genomic investigations of the vinegar fly, Drosophila melanogaster, have unravelled the functions of numerous genes of this model species, but results from phenomic experiments can sometimes be ambiguous. Moreover, the features underlying gene essentiality are poorly understood, posing challenges for computational prediction. Here, we harnessed comprehensive genomic-phenomic datasets publicly available for D. melanogaster and a machine-learning-based workflow to predict essential genes of this fly. We discovered strong predictors of such genes, paving the way for computational predictions of essentiality in less-studied arthropod pests and vectors of infectious diseases.

Computational Methods for Analysis of Large-Scale CRISPR Screens

Large-scale CRISPR-Cas pooled screens have shown great promise to investigate functional links between genotype and phenotype at the genome-wide scale. In addition to technological advancement, there is a need to develop computational methods to analyze the large datasets obtained from high-throughput CRISPR screens. Many computational methods have been developed to identify reliable gene hits from various screens. In this review, we provide an overview of the technology development of CRISPR screening platforms, with a focus on recent advances in computational methods to identify and model gene effects using CRISPR screen datasets. We also discuss existing challenges and opportunities for future computational methods development.

modEnrichr: a suite of gene set enrichment analysis tools for model organisms

High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The gene set libraries within FishEnrichr, FlyEnrichr, WormEnrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway databases, protein domain databases and other organism-specific resources. Additionally, libraries were created by predicting gene function from RNA-seq co-expression data processed uniformly from the gene expression omnibus for each organism. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species. For complex analyses, modEnrichr provides API access that enables submitting batch queries. In summary, modEnrichr leverages existing model organism databases and other resources to facilitate comprehensive hypothesis generation through data integration.

SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL² MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in SL² MF. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of SL² MF.

Hyd ubiquitinates the NF-κB co-factor Akirin to operate an effective immune response in Drosophila

The Immune Deficiency (IMD) pathway in Drosophila melanogaster is activated upon microbial challenge with Gram-negative bacteria to trigger the innate immune response. In order to decipher this nuclear factor κB (NF-κB) signaling pathway, we undertook an in vitro RNAi screen targeting E3 ubiquitin ligases specifically and identified the HECT-type E3 ubiquitin ligase Hyperplastic discs (Hyd) as a new actor in the IMD pathway. Hyd mediated Lys⁶³ (K63)-linked polyubiquitination of the NF-κB cofactor Akirin was required for efficient binding of Akirin to the NF-κB transcription factor Relish. We showed that this Hyd-dependent interaction was required for the transcription of immunity-related genes that are activated by both Relish and Akirin but was dispensable for the transcription of genes that depend solely on Relish. Therefore Hyd is key in NF-κB transcriptional selectivity downstream of the IMD pathway. Drosophila depleted of Akirin or Hyd failed to express the full set of genes encoding immune-induced anti-microbial peptides and succumbed to immune challenges. We showed further that UBR5, the mammalian homolog of Hyd, was also required downstream of the NF-κB pathway for the activation of Interleukin 6 (IL6) transcription by LPS or IL-1β in cultured human cells. Our findings link the action of an E3 ubiquitin ligase to the activation of immune effector genes, deepening our understanding of the involvement of ubiquitination in inflammation and identifying a potential target for the control of inflammatory diseases.

Ubiquitination has been recently identified in pathogenesis and progression of various diseases where inflammation is critical. NF-κB transcription factors are key actors in the transcriptional cascade leading to inflammation as they activate genes with pro- or anti-inflammatory activities. The similarity between the immune pathways in flies and mammals makes Drosophila melanogaster an excellent model to study the innate response. Accordingly, we decided to identify E3 ubiquitin-ligases involved in the regulation of NF-κB pathway, using Drosophila as a model system. A RNAi based screen in immortalized embryonic macrophage-like Drosophila cells points to the HECT-E3 ubiquitin ligase Hyd as a new regulator of the Immune-deficiency (IMD) NF-κB pathway, activated after Gram-negative immune challenge.

More precisely, we showed that Hyd acts at the level of Akirin, an evolutionarily conserved player in the NF-κB pathway, required for the transcription of pro-inflammatory genes, but not for the NF-κB-dependent genes contributing to the down-regulation of inflammation.

In addition, we could show that the human homologue of Hyd (UBR5) acts genetically at the level of human AKIRIN2, pointing to a unique dichotomy between Hyd/Akirin-dependent and -independent gene activation, allowing for the decoupling activation and resolution of inflammation. These results identified UBR5 as a putative target for anti-inflammatory compounds.

Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism

Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.

Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

BACKGROUND

Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.

RESULTS

In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.

CONCLUSIONS

In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.

The Architecture of the Human RNA-Binding Protein Regulatory Network

RNA-binding proteins (RBPs) are key players of post-transcriptional regulation of gene expression, relying on competitive and cooperative interactions to fine-tune their action. Several studies have described individual interactions of RBPs with RBP mRNAs. Here we present a systematic network investigation of fifty thousand interactions between RBPs and the UTRs of RBP mRNAs. We identified two structural features in this network. RBP clusters are groups of densely interconnected RBPs co-binding their targets, suggesting a tight control of cooperative and competitive behaviors. RBP chains are hierarchical structures connecting RBP clusters and driven by evolutionarily ancient RBPs. These features suggest that RBP chains may coordinate the different cell programs controlled by RBP clusters. Under this model, the regulatory signal flows through chains from one cluster to another, implementing elaborate regulatory plans. This work thus suggests RBP-RBP interactions as a backbone driving post-transcriptional regulation of gene expression to control RBPs action on their targets.

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The RBP-RBP network is a robust and efficient hierarchical structure

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The RBP-RBP network is formed by RBP clusters and chains

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RBP-RBP interactions cluster to cooperate and compete on common target mRNAs

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RBP chains are master regulatory units of the cell led by ancient RBPs

Common Nodes of Virus-Host Interaction Revealed Through an Integrated Network Analysis

Viruses are one of the major causes of acute and chronic infectious diseases and thus a major contributor to the global burden of disease. Several studies have shown how viruses have evolved to hijack basic cellular pathways and evade innate immune response by modulating key host factors and signaling pathways. A collective view of these multiple studies could advance our understanding of virus-host interactions and provide new therapeutic perspectives for the treatment of viral diseases. Here, we performed an integrative meta-analysis to elucidate the 17 different host-virus interactomes. Network and bioinformatics analyses showed how viruses with small genomes efficiently achieve the maximal effect by targeting multifunctional and highly connected host proteins with a high occurrence of disordered regions. We also identified the core cellular process subnetworks that are targeted by all the viruses. Integration with functional RNA interference (RNAi) datasets showed that a large proportion of the targets are required for viral replication. Furthermore, we performed an interactome-informed drug re-purposing screen and identified novel activities for broad-spectrum antiviral agents against hepatitis C virus and human metapneumovirus. Altogether, these orthogonal datasets could serve as a platform for hypothesis generation and follow-up studies to broaden our understanding of the viral evasion landscape.

siRNA-Finder (si-Fi) Software for RNAi-Target Design and Off-Target Prediction

RNA interference (RNAi) is a technique used for transgene-mediated gene silencing based on the mechanism of posttranscriptional gene silencing (PTGS). PTGS is an ubiquitous basic biological phenomenon involved in the regulation of transcript abundance and plants' immune response to viruses. PTGS also mediates genomic stability by silencing of retroelements. RNAi has become an important research tool for studying gene function by strong and selective suppression of target genes. Here, we present si-Fi, a software tool for design optimization of RNAi constructs necessary for specific target gene knock-down. It offers efficiency prediction of RNAi sequences and off-target search, required for the practical application of RNAi. si-Fi is an open-source (CC BY-SA license) desktop software that works in Microsoft Windows environment and can use custom sequence databases in standard FASTA format.

High-throughput screening for negative-stranded hemorrhagic fever viruses using reverse genetics

Viral hemorrhagic fevers (VHFs) cause thousands of fatalities every year, but the treatment options for their management remain very limited. In particular, the development of therapeutic interventions is restricted by the lack of commercial viability of drugs targeting individual VHF agents. This makes approaches like drug repurposing and/or the identification of broad range therapies (i.e. those directed at host responses or common proviral factors) highly attractive. However, the identification of candidates for such antiviral repurposing or of host factors/pathways important for the virus life cycle is reliant on high-throughput screening (HTS). Recently, such screening work has been increasingly facilitated by the availability of reverse genetics-based approaches, including tools such as full-length clone (FLC) systems to generate reporter-expressing viruses or various life cycle modelling (LCM) systems, many of which have been developed and/or greatly improved during the last years. In particular, since LCM systems are capable of modelling specific steps in the life cycle, they are a valuable tool for both targeted screening (i.e. for inhibitors of a specific pathway) and mechanism of action studies. This review seeks to summarize the currently available reverse genetics systems for negative-sense VHF causing viruses (i.e. arenaviruses, bunyaviruses and filoviruses), and to highlight the recent advancements made in applying these systems for HTS to identify either antivirals or new virus-host interactions that might hold promise for the development of future treatments for the infections caused by these deadly but neglected virus groups.

A Secreted Ig-Domain Protein Required in Both Astrocytes and Neurons for Regulation of Drosophila Night Sleep

Endogenous rhythmic behaviors are evolutionarily conserved and essential for life. In mammalian and invertebrate models, well-characterized neuronal circuits and evolutionarily conserved mechanisms regulate circadian behavior and sleep [1-4]. In Drosophila, neuronal populations located in multiple brain regions mediate arousal, sleep drive, and homeostasis (reviewed in [3, 5-7]). Similar to mammals [8], there is also evidence that fly glial cells modulate the neuronal circuits controlling rhythmic behaviors, including sleep [1]. Here, we describe a novel gene (CG14141; aka Nkt) that is required for normal sleep. NKT is a 162-amino-acid protein with a single IgC2 immunoglobulin (Ig) domain and a high-quality signal peptide [9], and we show evidence that it is secreted, similar to its C. elegans ortholog (OIG-4) [10]. We demonstrate that Nkt-null flies or those with selective knockdown in either neurons or glia have decreased and fragmented night sleep, indicative of a non-redundant requirement in both cell types. We show that Nkt is required in fly astrocytes and in a specific set of wake-promoting neurons-the mushroom body (MB) α'β' cells that link sleep to memory consolidation [11]. Importantly, Nkt gene expression is required in the adult nervous system for normal sleep, consistent with a physiological rather than developmental function for the Ig-domain protein.

Proteomic analysis of the effect of the polyphenol pentagalloyl glucose on proteins involved in neurodegenerative diseases in activated BV‑2 microglial cells

Neuroinflammation and microglial activation are two important hallmarks of neurodegenerative diseases. Continuous microglial activation may cause the release of several cytotoxic molecules, including many cytokines that are involved in the inflammatory process. Therefore, attenuating inflammation caused by activated microglia may be an approach for the therapeutic management of neurodegenerative diseases. In addition, many studies have reported that polyphenol pentagalloyl glucose (1,2,3,4,6-penta-O-galloyl-β-D-glucose; PGG) is a molecule with potent anti-inflammatory effects, such as inhibiting the release of proinflammatory cytokines. Our previous studies revealed that PGG attenuated the expression of two inflammatory cytokines (murine monocyte chemoattractant protein-5 and pro-metalloproteinase-9) in lipopolysaccharide/interferon γ-activated BV-٢ microglial cells. Additionally, PGG modulated the NF-κB and MAPK signaling pathways by altering genes and proteins, which may affect the MAPK cascade and NF-κB activation. The aim of the present study was to investigate the ability of PGG to modulate the expression of proteins released in activated BV-2 microglial cells, which may be involved in the pathological process of inflammation and neurodegeneration. Proteomic analysis of activated BV-2 cells identified 17 proteins whose expression levels were significantly downregulated by PGG, including septin-7, ataxin-2, and adenylosuccinate synthetase isozyme 2 (ADSS). These proteins were previously described as being highly expressed in neurodegenerative diseases and/or involved in the signaling pathways associated with the formation and growth of neuronal connections and the control of Alzheimer's disease pathogenesis. The inhibitory effect of PGG on ataxin-2, septin-7 and ADSS was further confirmed at the protein and transcriptional levels. Therefore, the obtained results suggest that PGG, with its potent inhibitory effects on ataxin-2, septin-7 and ADSS, may have potential use in the therapeutic management of neurodegenerative diseases.

A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells

BACKGROUND

The introduction of genome-wide shRNA and CRISPR libraries has facilitated cell-based screens to identify loss-of-function mutations associated with a phenotype of interest. Approaches to perform analogous gain-of-function screens are less common, although some reports have utilized arrayed viral expression libraries or the CRISPR activation system. However, a variety of technical and logistical challenges make these approaches difficult for many labs to execute. In addition, genome-wide shRNA or CRISPR libraries typically contain of hundreds of thousands of individual engineered elements, and the associated complexity creates issues with replication and reproducibility for these methods.

RESULTS

Here we describe a simple, reproducible approach using the SB transposon system to perform phenotypic cell-based genetic screens. This approach employs only three plasmids to perform unbiased, whole-genome transposon mutagenesis. We also describe a ligation-mediated PCR method that can be used in conjunction with the included software tools to map raw sequence data, identify candidate genes associated with phenotypes of interest, and predict the impact of recurrent transposon insertions on candidate gene function. Finally, we demonstrate the high reproducibility of our approach by having three individuals perform independent replicates of a mutagenesis screen to identify drivers of vemurafenib resistance in cultured melanoma cells.

CONCLUSIONS

Collectively, our work establishes a facile, adaptable method that can be performed by labs of any size to perform robust, genome-wide screens to identify genes that influence phenotypes of interest.

Comparing signals of natural selection between three Indigenous North American populations

While many studies have highlighted human adaptations to diverse environments worldwide, genomic studies of natural selection in Indigenous populations in the Americas have been absent from this literature until very recently. Since humans first entered the Americas some 20,000 years ago, they have settled in many new environments across the continent. This diversity of environments has placed variable selective pressures on the populations living in each region, but the effects of these pressures have not been extensively studied to date. To help fill this gap, we collected genome-wide data from three Indigenous North American populations from different geographic regions of the continent (Alaska, southeastern United States, and central Mexico). We identified signals of natural selection in each population and compared signals across populations to explore the differences in selective pressures among the three regions sampled. We find evidence of adaptation to cold and high-latitude environments in Alaska, while in the southeastern United States and central Mexico, pathogenic environments seem to have created important selective pressures. This study lays the foundation for additional functional and phenotypic work on possible adaptations to varied environments during the history of population diversification in the Americas.

A polymorphic MYC response element in KBTBD11 influences colorectal cancer risk, especially in interaction with an MYC-regulated SNP rs6983267

Background

MYC is a well-established cancer driver gene regulating the expression of numerous genes, indicating that polymorphisms in MYC response elements could affect tumorigenesis through altering MYC regulation. We performed integrative multistage study to evaluate the effects of variants in MYC response elements and colorectal cancer (CRC) risk.

Patients and methods

We systematically integrated ChIP-Seq, DNase-Seq and transcription factor motif data to screen variants with potential ability to affect the MYC binding affinity. Then, we conducted a two-stage case-control study, totally consisting of 4830 CRC cases and 4759 controls in Chinese population to identify risk polymorphisms and interactions. The effects of risk variants were confirmed by functional assays in CRC LoVo, SW480 and HCT15 cells.

Results

We identified a novel polymorphism rs11777210 in KBTBD11 significantly associated with CRC susceptibility (P = 2.43 × 10-12). Notably, we observed a significant interaction between rs11777210 and MYC nearby rs6983267 (P-multi = 0.003, P-add = 0.005), subjects carrying rs6983267 GG and rs11777210 CC genotypes showing higher susceptibility to CRC (2.83-fold) than those carrying rs6983267 TT and rs11777210 TT genotypes. We further demonstrated that rs6983267 T > G increased MYC expression, and MYC bound to and negatively regulated KBTBD11 expression when the rs11777210 C risk allele was present. KBTBD11 was downregulated in tumor tissues, and KBTBD11 knockdown promoted cell proliferation and inhibited cell apoptosis.

Conclusion

The rs11777210 is a potential predictive biomarker of CRC susceptibility, and KBTBD11 functions as a putative tumor suppressor in tumorigenesis. Our study highlighted the high CRC risk of people carrying rs6983267 G and rs11777210 C alleles, and provided possible biological mechanism of the interaction.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Large-scale investigation of the reasons why potentially important genes are ignored

Biomedical research has been previously reported to primarily focus on a minority of all known genes. Here, we demonstrate that these differences in attention can be explained, to a large extent, exclusively from a small set of identifiable chemical, physical, and biological properties of genes. Together with knowledge about homologous genes from model organisms, these features allow us to accurately predict the number of publications on individual human genes, the year of their first report, the levels of funding awarded by the National Institutes of Health (NIH), and the development of drugs against disease-associated genes. By explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, we describe gene-specific strategies for the identification of important but hitherto ignored genes that can open novel directions for future investigation.

RNAi/CRISPR Screens: from a Pool to a Valid Hit

High-throughput genetic screens interfering with gene expression are invaluable tools to identify gene function and phenotype-to-genotype interactions. Implementing such screens in the laboratory is challenging, and the choice between currently available technologies based on RNAi and CRISPR/Cas9 (CRISPR-associated protein 9) is not trivial. Identifying reliable candidate hits requires a streamlined experimental setup adjusted to the specific biological question. Here, we provide a critical assessment of the various RNAi/CRISPR approaches to pooled screens and discuss their advantages and pitfalls. We specify a set of best practices for key parameters enabling a reproducible screen and provide a detailed overview of analysis methods and repositories for identifying the best candidate gene hits.

A genome-wide siRNA screen identifies a druggable host pathway essential for the Ebola virus life cycle

Background

The 2014–2016 Ebola virus (EBOV) outbreak in West Africa highlighted the need for improved therapeutic options against this virus. Approaches targeting host factors/pathways essential for the virus are advantageous because they can potentially target a wide range of viruses, including newly emerging ones and because the development of resistance is less likely than when targeting the virus directly. However, systematic approaches for screening host factors important for EBOV have been hampered by the necessity to work with this virus at biosafety level 4 (BSL4).

Methods

In order to identify host factors involved in the EBOV life cycle, we performed a genome-wide siRNA screen comprising 64,755 individual siRNAs against 21,566 human genes to assess their activity in EBOV genome replication and transcription. As a screening platform, we used reverse genetics-based life cycle modelling systems that recapitulate these processes without the need for a BSL4 laboratory.

Results

Among others, we identified the de novo pyrimidine synthesis pathway as an essential host pathway for EBOV genome replication and transcription, and confirmed this using infectious EBOV under BSL4 conditions. An FDA-approved drug targeting this pathway showed antiviral activity against infectious EBOV, as well as other non-segmented negative-sense RNA viruses.

Conclusions

This study provides a minable data set for every human gene regarding its role in EBOV genome replication and transcription, shows that an FDA-approved drug targeting one of the identified pathways is highly efficacious in vitro, and demonstrates the power of life cycle modelling systems for conducting genome-wide host factor screens for BSL4 viruses.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0570-1) contains supplementary material, which is available to authorized users.

saRNAdb: Resource of Small Activating RNAs for Up-regulating the Gene Expression

RNA activation (RNAa) is the process of enhancing selective gene expression at transcriptional level using double-stranded RNAs, targeting gene promoter. These RNA molecules are usually 21 nucleotides long and termed as small activating RNAs (saRNAs). They are involved in gene regulation, epigenetics, gain-of-function studies and have potential therapeutic applications for various diseases especially cancer. RNAa is opposite to RNA interference in functionality; however, both processes share some protein machinery. There are many RNA interference centered online resources but no one for saRNAs; therefore, we developed "saRNAdb" database (http://bioinfo.imtech.res.in/manojk/sarna/). It contains 2150 manually curated saRNA entries with detailed information about their nucleotide sequences, activities, corresponding target gene, promoter and other experimental data. Besides, saRNA-promoter binding location, predicted saRNA features, tools (off-target, map) and RNAa-related proteins with their interacting partners are provided. saRNAdb is expected to assist in RNA research especially for nucleic acid-based therapeutics development.

vhfRNAi: a web-platform for analysis of host genes involved in viral infections discovered by genome wide RNAi screens

Knockdown of host genes using high-throughput genome-wide RNA interference screens has identified numerous host factors that affect viral infections, which would be helpful in understanding host-virus interactions. We have developed a vhfRNAi web resource based on genome-wide RNAi experiments for viruses. It contains experimental details of 12 249 entries (host factors + restriction factors) for 18 viruses. Simultaneously, this resource encompasses analysis of overlapping genes, genome wide association studies, gene ontology (GO), pathogen interacting proteins, interaction networks and pathway enrichment. Using overlap analysis, it was found that Influenza A virus shared overlapping host genes with the majority of viruses including Hepatitis C virus and Dengue virus 2. In the genome wide association studies analysis, 429 diseases/traits were mapped, of which obesity-related traits were the most common. GO analysis revealed that the major categories belonged to metabolic processes, molecule transport, signal transduction, proteolysis, etc. In the pathogen interacting protein analysis, protein interaction data from different resources can be explored for further understanding of host-virus biology. By pathway enrichment analysis, a total of 8955 genes were mapped on 303 pathways with most of the hits coming from metabolic pathways. We have found 491 genes that are not essential for the host but essential for the virus and can be targeted to inhibit the virus. These may be explored as potential candidates for drug targets. The resource is freely accessible at and will be useful in understanding host-virus biology as well as identification of targets for the development of antiviral therapeutics.

The chromatin-remodeling factor CHD4 is required for maintenance of childhood acute myeloid leukemia

Epigenetic alterations contribute to leukemogenesis in childhood acute myeloid leukemia and therefore are of interest for potential therapeutic strategies. Herein, we performed large-scale ribonucleic acid interference screens using small hairpin ribonucleic acids in acute myeloid leukemia cells and non-transformed bone marrow cells to identify leukemia-specific dependencies. One of the target genes displaying the strongest effects on acute myeloid leukemia cell growth and less pronounced effects on nontransformed bone marrow cells, was the chromatin remodeling factor CHD4 Using ribonucleic acid interference and CRISPR-Cas9 approaches, we showed that CHD4 was essential for cell growth of leukemic cells in vitro and in vivo Loss of function of CHD4 in acute myeloid leukemia cells caused an arrest in the G0 phase of the cell cycle as well as downregulation of MYC and its target genes involved in cell cycle progression. Importantly, we found that inhibition of CHD4 conferred anti-leukemic effects on primary childhood acute myeloid leukemia cells and prevented disease progression in a patient-derived xenograft model. Conversely, CHD4 was not required for growth of normal hematopoietic cells. Taken together, our results identified CHD4 as a potential therapeutic target in childhood acute myeloid leukemia.